{"id":"W2130344546","doi":"10.1145/1181775.1181777","title":"Using task context to improve programmer productivity","year":2006,"lang":"en","type":"article","venue":"","topic":"Personal Information Management and User Behavior","field":"Decision Sciences","cited_by":447,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Programmer; Computer science; Task (project management); Context (archaeology); Human–computer interaction; Task switching; Task management; Task analysis; Software engineering; Programming language; Systems engineering; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00110655,0.00009753414,0.0001339792,0.0002016313,0.0001201856,0.000442917,0.0003160973,0.00002675445,0.0004463432],"category_scores_gemma":[0.0001655017,0.00006695563,0.00006809323,0.000634264,0.00003405529,0.0008261171,0.0001230454,0.00004904611,0.001073062],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003233968,"about_ca_system_score_gemma":0.00002329317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005284311,"about_ca_topic_score_gemma":0.0002623168,"domain_scores_codex":[0.9982628,0.00003151518,0.0003755255,0.000296038,0.0008081003,0.00022603],"domain_scores_gemma":[0.9991895,0.00004762878,0.0001000392,0.0003441412,0.0002478445,0.0000708256],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00005812401,0.0002990983,0.07772435,0.000005954138,0.00001197542,0.000005247336,0.0005628963,0.0003091349,0.0220294,0.02349156,0.1503512,0.7251511],"study_design_scores_gemma":[0.0005320688,0.0001025176,0.1067029,0.000005681351,0.00002410811,0.000003812552,0.001797413,0.005325488,0.008779356,0.004560853,0.8717125,0.0004532929],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9552172,0.000008910351,0.02234763,0.001655887,0.0003388666,0.0005541489,0.000006786246,0.0000775356,0.01979307],"genre_scores_gemma":[0.9676463,4.993451e-8,0.004690192,0.0005514664,0.0001291178,0.0000189178,0.000002417213,0.000004694848,0.02695683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7246978,"threshold_uncertainty_score":0.9997047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3139545480922422,"score_gpt":0.4520012210452668,"score_spread":0.1380466729530246,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}